Python中單線程、多線程和多進程的效率對比實驗

對比實驗

資料顯示，如果多線程的進程是CPU密集型的，那多線程並不能有多少效率上的提升，相反還可能會因為線程的頻繁切換，導致效率下降，推薦使用多進程；如果是IO密集型，多線程進程可以利用IO阻塞等待時的空閑時間執行其他線程，提升效率。所以我們根據實驗對比不同情境的效率

(1)引入所需要的模組

import requestsimport timefrom threading import Threadfrom multiprocessing import Process

(2)定義CPU密集的計算函數

def count(x, y):    # 使程式完成150萬計算    c = 0    while c < 500000:        c += 1        x += x        y += y

（3）定義IO密集的檔案讀寫函數

def write():    f = open("test.txt", "w")    for x in range(5000000):        f.write("testwrite\n")    f.close()def read():    f = open("test.txt", "r")    lines = f.readlines()    f.close()

(4) 定義網路請求函數

_head = {            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.116 Safari/537.36'}url = "http://www.tieba.com"def http_request():    try:        webPage = requests.get(url, headers=_head)        html = webPage.text        return {"context": html}    except Exception as e:        return {"error": e}

(5)測試線性執行IO密集操作、CPU密集操作所需時間、網路請求密集型操作所需時間

# CPU密集操作t = time.time()for x in range(10):    count(1, 1)print("Line cpu", time.time() - t)# IO密集操作t = time.time()for x in range(10):    write()    read()print("Line IO", time.time() - t)# 網路請求密集型操作t = time.time()for x in range(10):    http_request()print("Line Http Request", time.time() - t)

輸出

CPU密集：95.6059999466、91.57099986076355 92.52800011634827、 99.96799993515015

IO密集：24.25、21.76699995994568、21.769999980926514、22.060999870300293

網路請求密集型: 4.519999980926514、8.563999891281128、4.371000051498413、4.522000074386597、14.671000003814697

(6)測試多線程並發執行CPU密集操作所需時間

counts = []t = time.time()for x in range(10):    thread = Thread(target=count, args=(1,1))    counts.append(thread)    thread.start()e = counts.__len__()while True:    for th in counts:        if not th.is_alive():            e -= 1    if e <= 0:        breakprint(time.time() - t)

Output: 99.9240000248 、101.26400017738342、102.32200002670288

(7)測試多線程並發執行IO密集操作所需時間

def io():    write()    read()t = time.time()ios = []t = time.time()for x in range(10):    thread = Thread(target=count, args=(1,1))    ios.append(thread)    thread.start()e = ios.__len__()while True:    for th in ios:        if not th.is_alive():            e -= 1    if e <= 0:        breakprint(time.time() - t)

Output: 25.69700002670288、24.02400016784668

(8)測試多線程並發執行網路密集操作所需時間

t = time.time()ios = []t = time.time()for x in range(10):    thread = Thread(target=http_request)    ios.append(thread)    thread.start()e = ios.__len__()while True:    for th in ios:        if not th.is_alive():            e -= 1    if e <= 0:        breakprint("Thread Http Request", time.time() - t)

Output: 0.7419998645782471、0.3839998245239258、0.3900001049041748

(9)測試多進程並發執行CPU密集操作所需時間

counts = []t = time.time()for x in range(10):    process = Process(target=count, args=(1,1))    counts.append(process)    process.start()e = counts.__len__()while True:    for th in counts:        if not th.is_alive():            e -= 1    if e <= 0:        breakprint("Multiprocess cpu", time.time() - t)

Output: 54.342000007629395、53.437999963760376

(10)測試多進程並發執行IO密集型操作

t = time.time()ios = []t = time.time()for x in range(10):    process = Process(target=io)    ios.append(process)    process.start()e = ios.__len__()while True:    for th in ios:        if not th.is_alive():            e -= 1    if e <= 0:        breakprint("Multiprocess IO", time.time() - t)

Output: 12.509000062942505、13.059000015258789

(11)測試多進程並發執行Http請求密集型操作

t = time.time()httprs = []t = time.time()for x in range(10):    process = Process(target=http_request)    ios.append(process)    process.start()e = httprs.__len__()while True:    for th in httprs:        if not th.is_alive():            e -= 1    if e <= 0:        breakprint("Multiprocess Http Request", time.time() - t)

Output: 0.5329999923706055、0.4760000705718994

實驗結果

通過上面的結果，我們可以看到：

多線程在IO密集型的操作下似乎也沒有很大的優勢（也許IO操作的任務再繁重一些就能體現出優勢），在CPU密集型的操作下明顯地比單線程線性執行效能更差，但是對於網路請求這種忙等阻塞線程的操作，多線程的優勢便非常顯著了

多進程無論是在CPU密集型還是IO密集型以及網路請求密集型（經常發生線程阻塞的操作）中，都能體現出效能的優勢。不過在類似網路請求密集型的操作上，與多線程相差無幾，但卻更佔用CPU等資源，所以對於這種情況下，我們可以選擇多線程來執行

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